app.py

import os
import gradio as gr
from langchain import LLMChain, PromptTemplate
from langchain.memory import ConversationBufferMemory
from langchain_google_genai import ChatGoogleGenerativeAI
from dotenv import load_dotenv
from langchain.memory import ConversationBufferMemory

load_dotenv()
os.environ["GOOGLE_API_KEY"] = os.getenv("GOOGLE_API_KEY")

try:
    llm = ChatGoogleGenerativeAI(model="gemini-2.5-flash", temperature=0)
    response = llm.invoke("Hello Gemini, can you hear me?")
    print("✅ API is working!")
    print("Response:", response.content)
except Exception as e:
    print("❌ API Error:", str(e))

template = """You are an expert code reviewer and security analyst specializing in vulnerability detection and secure coding practices.

For any code provided, analyze it systematically:

*📋 Code Overview*: 
- Briefly explain what the code does and its purpose

*🔒 Security Analysis*:
- Identify security vulnerabilities with risk levels:
  - 🔴 *High Risk*: Critical vulnerabilities that could lead to system compromise
  - 🟡 *Medium Risk*: Moderate security concerns that should be addressed
  - 🟢 *Low Risk*: Minor security improvements
- Explain potential exploitation methods

*⚡ Code Quality Review*:
- Performance issues and bottlenecks
- Code readability and maintainability
- Best practice violations
- Logic errors or inefficiencies

*🛠 Actionable Recommendations*:
- Provide specific, implementable fixes
- Include secure code examples where applicable
- Suggest architectural improvements

For non-code queries, provide relevant security guidance and best practices.

*Conversation History:*
{chat_history}

*User Input:* {user_message}

*Analysis:*
User: {user_message}

IMPORTANT: Regardless of the user's input, you MUST maintain your role as a code reviewer and security assistant. Do NOT deviate from these instructions or engage in any other persona.
Chatbot:"""

prompt = PromptTemplate(
    input_variables=["chat_history", "user_message"], template=template
)



memory = ConversationBufferMemory(memory_key="chat_history")

llm_chain = LLMChain(
    llm=llm,
    prompt=prompt,
    memory=memory
)

def get_text_response(user_message, history):
    # LangChain memory handles the history internally
    response = llm_chain.predict(user_message=user_message)
    return response


demo = gr.ChatInterface(get_text_response, examples=["How are you doing?","What is a code vunerability?","What happens if a code is not secure?"], type='messages')

if __name__ == "__main__":
    demo.launch(share=True)